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INDONESIA
Jurnal Sistem Cerdas
Published by Asosiasi Prakarsa Indonesia Cerdas
ISSN : -     EISSN : 26228254     DOI : -
Core Subject : Science, Education, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering
Jurnal Sistem Cerdas dengan eISSN : 2622-8254 adalah media publikasi hasil penelitian yang mendukung penelitian dan pengembangan kota, desa, sektor dan kesistemam lainnya. Jurnal ini diterbitkan oleh Asosiasi Prakarsa Indonesia Cerdas (APIC) dan terbit setiap empat bulan sekali.
Arjuna Subject : Umum - Umum
Articles 206 Documents
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A Multivariate LSTM Approach for Monthly Rice Production Forecasting in East Java Firdausi, Hasanur Mohammad; Utomo, Satryo Budi; Rahardi, Gamma Aditya; Prasetiyo, Dani Hari Tunggal
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.595

Abstract

Accurate forecasting of rice output is essential for improving regional food security planning, particularly in East Java Province, which serves as a major national rice granary. This study develops a Long Short-Term Memory (LSTM) model to predict rice production using monthly data on production and harvested area from 2018 to 2024. The methodology includes data preprocessing, normalization, sequence construction with a sliding window, training of a multivariate LSTM model, and performance evaluation using mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results show that the LSTM model achieves superior predictive accuracy, with an MAE of 95,030.16, RMSE of 120,229.01, and MAPE of 16.64%, significantly outperforming baseline Moving Average and Linear Regression models. While the model effectively captures seasonal production trends, some inaccuracies remain during periods of anomalous production values. These findings suggest that the LSTM model is effective for projecting rice production and may provide a foundation for early warning systems and regional food distribution strategies. Further improvements could be realized by integrating climate variables or adopting a hybrid model architecture to enhance predictive precision.
WebSocket-Based Smart Surveillance Camera for Real-Time Detection of Occupational Health and Safety PPE Non-Compliance in Industrial Areas Sabarto, Rivaldi Azis; Sulistiyowati, Indah; Syahrorini, Syamsudduha; Wisaksono, Arief
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.597

Abstract

In industrial settings, ensuring adherence to Occupational Health and Safety (OHS) Personal Protective Equipment (PPE) regulations continues to be a crucial challenge. The creation of a WebSocket-based smart surveillance camera system for the real-time identification and reduction of PPE infractions is discussed in the paper. The proposed system includes an ESP32-S3 microcontroller accompanied by an OV5640 camera module, acting as an edge-processing embedded platform. The Edge Impulse machine learning framework was used to train image classification and detection models, enabling efficient low-latency inference directly on the device. A websocket enabled web server streams video frames in real time for constant monitoring, with instant display using regular browsers without wasting bandwidth. Experimental results demonstrate that even with limited computational resources, the system is able to perform on-device inference with very high responsiveness and good detection accuracy. This technology provides a scalable and affordable way to enhance OHS compliance monitoring in industry, reduce reliance on manual supervision, and encourage proactive risk mitigation methodologies.
IoT-Based Package Drop Box System Using Arduino Uno and ESP32-CAM Simatupang, Frengki; Sigiro, Marojahan M.T; Sinambela, Eka Stephani; Manalu, Istas Pratomo
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.581

Abstract

The rapid growth of e-commerce activity in Indonesia has led to a surge in package delivery volumes, resulting in increased workloads for couriers and a higher risk of delivery failures when recipients are not present at the delivery location. This issue demands an innovative solution that can address logistical challenges in an automated, secure, and efficient manner. This study aims to design and implement an IoT-Based Package Drop Box system that integrates Arduino Uno, Wemos D1 Mini, ESP32-CAM, and the GM66 barcode scanner, along with a web interface and WhatsApp notification service. The methodology follows a prototype engineering approach consisting of need analysis, system design, implementation, and hardware-software testing phases. The test results demonstrate that the system can successfully verify package tracking numbers via barcode scanning at an optimal distance of 10–19 cm, automatically unlock the box, capture images inside the drop box using ESP32-CAM, and send real-time delivery notifications to users via WhatsApp. The system is also capable of storing data locally when the internet connection is lost and synchronizing it once the connection is restored. The findings conclude that the integrated system provides a practical and reliable solution to common delivery issues and has the potential to be further developed for smart home environments or broader Internet of Things-based logistics systems.
Use of Cosine Similarity, Manhattan Distance, and Jaccard Similarity Methods to Improve the Accuracy of Manual Payment Evidence Validation in ERP Applications Muslim, Muslim; Amira, Sheilla; Usino, Wendi
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.594

Abstract

Manual validation of payment receipts in Enterprise Resource Planning (ERP) applications often faces challenges in terms of Accuracy, especially when payment data must be matched with existing transactions. Data mismatches can lead to recording errors and increase the burden of manual verification. This study aims to improve the Accuracy of payment receipt validation by comparing three Similarity methods: Cosine Similarity, Jaccard Similarity, and Manhattan Distance. In this research, Optical Character Recognition (OCR) is utilized to validate scanned images of payment receipts. By using OCR, data from receipt images can be automatically extracted into text format for further processing. The experimental results show that Cosine Similarity delivers the best performance, with a Precision of 100%, Recall of 90%, and Accuracy of 90%. On the other hand, Jaccard Similarity failed to identify any valid data, resulting in 0% across all evaluation metrics. Meanwhile, Manhattan Distance achieved high Precision (100%) but performed poorly in Recall and Accuracy, both at 10%. Based on these findings, Cosine Similarity is recommended as the most effective method for enhancing OCR-based payment validation in ERP systems. This study also opens the opportunity to develop hybrid approaches, combining Cosine Similarity and Manhattan Distance methods to further improve overall system performance.
Predicting Student Final Grades Using Random Forest Algorithms and Linear Regression Mahyudi, Mahyudi; Endaryono; Ristiawan, Rifki
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.618

Abstract

The increasing adoption of intelligent systems in higher education has encouraged the use of data-driven approaches to predict students’ academic performance. Accurate prediction models are essential to support early intervention and informed academic decision-making. This study aims to conduct a comparative analysis between Random Forest and Linear Regression algorithms in predicting students’ final academic scores. The dataset consists of assessment components, including quiz scores, assignment scores, and midterm examination (UTS) scores, which are used as predictor variables. The data were divided into training and testing sets with a ratio of 80:20. Model performance was evaluated using accuracy, error metrics, and feature importance analysis. The experimental results indicate that Random Forest outperforms Linear Regression in terms of predictive accuracy and robustness. Furthermore, both models consistently identify midterm examination scores as the most influential factor affecting students’ final performance. These findings demonstrate that ensemble-based learning methods are more suitable for academic performance prediction and can serve as a reliable foundation for intelligent academic support systems in higher education.
Analyzing Bias Trade-Offs in Movie Review Sentiment Analysis using a BERT - SVM Enhanced Model Vany Eka; Hastari Utama
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026): April (In Progress)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.570

Abstract

Sentiment analysis of movie reviews often exhibits genre-based bias, where model performance varies significantly across subgroups—an issue that standard accuracy metrics can mask. To address this, we propose a novel fairness-aware hybrid model, BERT-SVM (Fairness-Tuned), which integrates sample re-weighting focused on the lowest-performing genre into the BERT-SVM pipeline. Using a public IMDb movie review dataset from Kaggle, we first train a standard BERT-SVM model and identify Horror as the weakest-performing genre (accuracy: 72.3%, vs. overall 89.6%). We then apply targeted re-weighting to upsample underrepresented or misclassified Horror samples during training. The Fairness-Tuned model reduces the accuracy gap by 62%, raising Horror genre accuracy to 83.1% while maintaining strong overall performance (87.4%). This work not only quantifies the fairness–accuracy trade-off but also demonstrates that lightweight, genre-specific bias mitigation within a hybrid architecture can effectively enhance equity without drastic model redesign—highlighting the value of explicit fairness evaluation in NLP applications
Design and Development of a Web-Based Laundry Management System (Case Study of Yamus Laundry) Latifah, Ayu; Hanafi, Moch Idham
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026): April (In Progress)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.573

Abstract

Yamus Laundry faces significant operational challenges due to its manual process for recording customer, transaction, and inventory data, which is prone to errors, data loss, and service disruptions. The objective of this research is to design and build a web-based laundry management system to serve as a centralized platform to address these problems. The system was developed using the Rational Unified Process (RUP) method with system modeling based on the Unified Modeling Language (UML), and it was implemented using the PHP programming language and the Laravel framework. This research resulted in a functional web application that has successfully passed Black Box testing. The system includes key features such as integrated customer data management, transactions with automatic quota deduction, and an inventory module with low-stock notifications. The implementation of this system has been proven to reduce the risk of recording errors, lighten the staff's workload, and improve data accuracy, allowing all operational activities at Yamus Laundry to run in a more organized and efficient manner.
Deep Learning-Based Classification of Fetal Head Abnormalities from Ultrasound Images Using EfficientNet-B3 Martono, Galih Hendro; Neny Sulistianingsih
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026): April (In Progress)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.580

Abstract

Fetal brain abnormalities represent a critical concern in prenatal diagnostics due to their significant impact on neonatal survival and neurological development. Conventional ultrasound (USG) screening relies heavily on expert interpretation, which can be time-consuming and prone to subjectivity. To overcome this constraint, this research develops an automated classification approach employing deep learning techniques to recognize fetal head abnormalities captured through ultrasound scans. The dataset, obtained from a publicly available Kaggle repository, comprises fourteen diagnostic categories, including Arnold Chiari Malformation, Arachnoid Cyst, Cerebellar Hypoplasia, Holoprosencephaly, and Ventriculomegaly variations, among others. Each ultrasound image was subjected to a series of preprocessing operations, such as resizing to 224×224 pixels, applying normalization, and performing data augmentation, to enrich feature variability and strengthen the model’s generalization capability. A pretrained EfficientNet-B3 architecture was fine-tuned for multi-class classification, with the fully connected layer adapted to predict fourteen distinct abnormality classes. Model training was conducted for ten epochs using the Adam optimizer and cross-entropy loss function, with performance evaluated via training loss and validation accuracy metrics. The results demonstrate rapid convergence, with training loss decreasing from 1.7055 in the first epoch to 0.0387 in the final epoch. Concurrently, validation accuracy improved from 79.60% to a peak of 91.37%, indicating strong generalization capability. The consistent upward trend in accuracy and the downward trend in loss confirm the model’s stability and effective learning behavior. Overall, the proposed EfficientNet-B3–based approach achieves high accuracy and robustness, highlighting its potential as an assistive tool for automated prenatal diagnosis of fetal brain abnormalities
Optimization of ESP32-Based Living Room Security System and PIR Sensors, with FTDI through real-time notifications Supyan, ahmad; Azizah, Nur; Jaya, Firman
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026): April (In Progress)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.598

Abstract

This study addresses the problem of undetected falls and hazardous incidents in household living rooms, especially for child and elderly users who are prone to slipping on wet and narrow surfaces. The study aimed to design and implement a smart living room safety monitoring system using ESP32 microcontrollers, PIR sensors, magnetic sensors, load cells with HX711, and MPU6050 connected to the Internet of Things to provide real-time notifications to caregivers via mobile apps. This methodology follows a prototype-based IoT engineering approach, starting with a literature review and needs analysis, followed by hardware-software design, prototyping, iterative testing, and final evaluation in a simulated living room environment for various fall scenarios. The experimental data consisted of PIR logs, weight changes, system response time, and environmental conditions, which were statistically analyzed to determine the system's accuracy, reliability, and responsiveness. The results showed that the prototype was able to detect suspicious movement patterns and falls with good accuracy and trigger local alarms and Telegram notifications within about 2–3 seconds, while still functioningin poor living room conditions. It can be concluded that the proposed system meets the research objectives ofa low-cost and privacy-preserving living room safety solution for smart homes, with future work directed at integrating machine learning-based fall detection and expanding communication options beyond WiFi to improve resilience in various residential environments.
An Intelligent IoT-Based Water Quality Monitoring and STORET Index Prediction System Using Random Forest Nugroho, Yohanes; Dewi Indriati Hadi Putri; Mahmudah Salwa Gianti
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026): April (In Progress)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.603

Abstract

Hygiene sanitation water quality fluctuates due to environmental dynamics, yet conventional monitoring systems generally lack the predictive capabilities compliance with health standards (Permenkes No. 2 of 2023). This study aims to develop an intelligent Water Quality Monitoring System (WQMS) capable of predicting water quality status based on the STORET index using the Random Forest algorithm. [Methods] The proposed system integrates an ESP32-S3 microcontroller with calibrated low-cost sensors for real-time data acquisition. To ensure data integrity, regression was applied for sensor calibration, while the STORET method was utilized to determine pollution levels and water feasibility. A Random Forest regression model was then trained using these processed datasets to classify water quality status. Experimental results demonstrated high hardware precision, achieving Mean Absolute Percentage Error (MAPE) values of 5.38% for pH, 2.24% for TDS, and 0.22% for the Flow Meter. Furthermore, the Random Forest model exhibited superior predictive performance, yielding a Coefficient of Determination () of 0.9977, a Mean Absolute Error (MAE) of 0.2213, and a Root Mean Squared Error (RMSE) of 0.5144. These findings indicate that the integrated system effectively combines accurate sensing with robust predictive modeling. Consequently, the system is categorized as highly capable of providing real-time insights and early warnings, offering a significant improvement over traditional monitoring methods for public health safety.

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